A Survey of Feature Selection Techniques

Abstract— Feature selection is a term commonly used in data mining to describe the tools and techniques available for reducing inputs to a manageable size for processing and analysis. Feature selection implies not only cardinality reduction, which means imposing an arbitrary or predefined cutoff on the number of attributes that can be considered when building a model, but also the choice of attributes, meaning that either the analyst or the modeling tool actively selects or discards attributes based on their usefulness for analysis. Feature selection is an effective technique for dimension reduction and an essential step in successful data mining applications. It is a research area of great practical significance and has been developed and evolved to answer the challenges due to data of increasingly high dimensionality. The objective of feature selection is three fold. Improving the prediction performance of the predictors, Providing faster and more cost effective prediction and providing a better understanding of the underlying process that generate the data. This paper is actually a survey on various techniques of feature selection and its advantages and disadvantages.